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SETR_Naive_Base_768x768_80k_cityscapes_bs_8.py
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SETR_Naive_Base_768x768_80k_cityscapes_bs_8.py
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_base_ = [
'../_base_/models/setr_naive_pup.py',
'../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(img_size=768, align_corners=False,
pos_embed_interp=True, drop_rate=0.,
model_name='vit_base_patch16_384', embed_dim=768,
depth=12, num_heads=12),
decode_head=dict(img_size=768, embed_dim=768, align_corners=False, num_conv=2,
upsampling_method='bilinear', conv3x3_conv1x1=False, in_index=11),
auxiliary_head=[dict(
type='VisionTransformerUpHead',
in_channels=1024,
channels=512,
in_index=2,
img_size=768,
embed_dim=768,
num_classes=19,
norm_cfg=norm_cfg,
num_conv=2,
upsampling_method='bilinear',
align_corners=False,
conv3x3_conv1x1=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='VisionTransformerUpHead',
in_channels=1024,
channels=512,
in_index=5,
img_size=768,
embed_dim=768,
num_classes=19,
norm_cfg=norm_cfg,
num_conv=2,
upsampling_method='bilinear',
align_corners=False,
conv3x3_conv1x1=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='VisionTransformerUpHead',
in_channels=1024,
channels=512,
in_index=8,
img_size=768,
embed_dim=768,
num_classes=19,
norm_cfg=norm_cfg,
num_conv=2,
upsampling_method='bilinear',
align_corners=False,
conv3x3_conv1x1=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
])
optimizer = dict(lr=0.01, weight_decay=0.0,
paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})
)
crop_size = (768, 768)
test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512))
find_unused_parameters = True
data = dict(samples_per_gpu=1)